Single-point Values Research Articles

Interval prediction method for dynamic zoning of displacements in super-high arch dam under significant water level fluctuations

Displacement is one of the most intuitive indicators reflecting the influence of internal and external environments on dam structures. However, existing displacement monitoring models mostly rely on static fitting of single point and deterministic point value estimation, failing to deeply consider the similarity and uncertainty of displacement in dynamic changes under significant water level fluctuations. To address this issue, the displacements are first divided into three periods: ascending, descending, and stable periods. Second, based on the Ward criterion, the displacements of measuring points in different periods are clustered and partitioned, and the CRITIC method is applied to amalgamate the displacement of multiple points within each partition into comprehensive displacements, which reflect the displacement changes of the corresponding partition. Then, the comprehensive displacements are predicted utilizing deep learning model convolutional neural network-gated recurrent unit (CNN-GRU). Finally, to consider the uncertainty and randomness of displacement changes, prediction intervals based on Bootstrap method are employed to quantify the impact of uncertain factors. Engineering examples show that the proposed method can provide scientific basis and technical support for the safety monitoring and health diagnosis of super-high arch dams.

Association of changes in body composition with all-cause mortality in patients undergoing hemodialysis: A prospective cohort study

ObjectivesThis study aimed to explore the effect of longitudinal body composition changes on mortality risk in patients undergoing hemodialysis and identify whether changes in body composition can more accurately predict mortality than baseline status. MethodsA prospective cohort study was conducted on 340 patients undergoing hemodialysis. Lean mass and body fat were determined using a bioimpedance spectroscopy (BIS) device and expressed as the lean tissue index (LTI) or fat tissue index (FTI), respectively. The patients were subjected to BIS at baseline and after 1 year. The hazard ratio (HR) for death was calculated using Cox regression analysis. ResultsAmong 340 patients, 289 were tested with a repeat BIS. LTI loss and FTI gain were observed in 51.2% and 47.1% of the patients, respectively. Low baseline LTI was a significant predictor of all-cause mortality after adjusting for demographic and biochemical parameters (HR, 2.41; P = 0.047), but not when comorbidities were included in the multivariate analysis. However, after adjusting for various confounding factors, LTI loss (HR, 3.40; P = 0.039) and FTI gain (HR, 4.06; P = 0.024) were independent risk factors for all-cause mortality, and the adjusted HR for LTI loss and FTI gain vs. no LTI loss and no FTI gain was 5.34 (P = 0.016). ConclusionsLTI loss and FTI gain, particularly their combination, are important predictors of survival in patients undergoing hemodialysis. Our results emphasize that longitudinal changes in LTI and FTI are more strongly associated with all-cause mortality than single-point values. Therefore, it is important to dynamically assess the muscle and fat tissues and develop potential targeted treatment strategies for this population.

Gas exchange and 13C-based estimates of intrinsic water-use efficiency show different responses to CO2 enrichment: a global meta-analysis of experimental studies

ABSTRACT Tree intrinsic water-use efficiency (iWUE) is a key characteristic of ecosystem functions central to the global cycles of carbon, water, and energy. Gas exchange and stable carbon isotope compositions have been widely used to assess iWUE. However, it remains unclear to what extent the iWUE estimates obtained using different approaches are comparable, particularly in combination with changes in temperature, precipitation, and experimental duration. Herein, the responses of different estimators of iWUE (as A/g s or through isotopic composition of leaves or tree rings) to elevated CO2 concentrations (eCO2) were assessed through a meta-analysis of 361 observations from 98 independent experiments. Our results show that (1) different proxies for iWUE provide inconsistent estimates of the response of iWUE to eCO2. (2) Throughout the growing season, the average response of g s to eCO2 was smaller than the single-point values estimated by gas exchange and the eCO2-induced reduction in g s was stronger for evergreen species than for deciduous species. (3) Precipitation, rather than temperature, significantly affected the responses of 13C-based estimates of iWUE to eCO2. (4) There was no significant response of leaf 13C-based iWUE estimates to eCO2. (5) The importance of photorespiration in elevated CO2 enhancement responses may be overestimated in evergreen species.

Likelihood of Discovering a CSF Leak Based on Intracranial MRI Findings in Patients without a Spinal Longitudinal Extradural Collection: A New Probabilistic Scoring System.

The likelihood of discovering a CSF leak can be determined by assessing intracranial abnormalities. However, the Dobrocky scoring system, which is used to determine this likelihood, did not incorporate patients with CSF-venous fistulas. This study sought to create a new probabilistic scoring system applicable to patients without a spinal longitudinal extradural collection. A retrospective review was completed of patients with suspected spontaneous intracranial hypotension who underwent brain MR imaging followed by digital subtraction myelography with same-day CT myelography. Patients with and without leaks found on digital subtraction myelography were included. MRIs were assessed for numerous reported stigmata of spontaneous intracranial hypotension and were compared between cohorts. One hundred seventy-four patients were included; 113 (64.9%) were women (average age, 52.0 [SD, 14.3] years). A CSF leak was found in 98 (56.3%) patients, nearly all of which (93.9%) were CSF-venous fistulas. Diffuse dural enhancement, internal auditory canals dural enhancement, non-Chiari cerebellar descent, pituitary engorgement, brain sag, dural venous sinus engorgement, and decreased suprasellar cistern size were associated with a CSF leak. A probabilistic scoring system was made in which a single point value was assigned to each of those findings: 0-2 considered low probability and ≥3 considered intermediate-to-high probability of a CSF leak. This study offers a new probabilistic scoring system for evaluating the likelihood of discovering a CSF leak on the basis of intracranial MR imaging findings, though the new system is not superior to that of the Dobrocky method for predicting the presence of CSF leaks.

Calculating Cooled Turbine Efficiency With Weighted Cooling Flow Distributions

Abstract The ability to accurately characterize the thermal efficiency of turbine stages with cooled components is essential to advance turbine technologies. There are many methods for calculating aerodynamic efficiency of turbomachinery components, with most encompassing either single point or simple integrated values at the inlet and exit locations. For cooled turbine stages, there is an added complexity of how to incorporate cooling flow streams. Although these techniques may include multiple cooling flows, a fully mixed assumption is often invoked without directly accounting for distributions of cooling flow in the annulus. However, the distribution of cooling flows in turbine stages may vary significantly depending on the location and nature of injection. This study addresses how calculated efficiency may vary depending upon how cooling flows are injected and the manner in which these cooling flows are accounted. The analyses utilize experimentally measured cooling flow concentration profiles to motivate a new approach for calculating cooled turbine efficiency with weighted cooling flow distributions. Validation data for a one-stage high-pressure turbine geometry from the energy efficient engine (E3) program are assessed to determine the range of influence for cooling flow distributions. Results show that cooling flows injected at similar axial positions can have unequal influences on integrated stage efficiency based on known distributions of cooling flow and other properties at the turbine stage exit plane. In some cases, deviations approaching half a point of efficiency can be observed relative to traditional equations, and the magnitude of influence is shown to increase as stage total pressure ratio decreases.

Protein Alternatives for Use in Fish Feed – Life Cycle Assessment of Black Soldier Fly, Yellow Mealworm and Soybean Protein

Abstract More production is needed as fish consumption increases. The increasing demand for fish also has implications for fish feed, the efficiency of its production and sustainability. Three protein alternatives – Black Soldier Fly, Yellow Mealworm, and Soybean – are compared to assess the sustainability of raw materials and their environmental impact. Each alternative has advantages and disadvantages. The advantages of Black Soldier fly and Yellow Mealworm are a valuable source of protein, sustainable growth (as the feed can be used in food waste) and no need for arable land. The disadvantages of Black Soldier fly and Yellow Mealworm are cost, unbalanced diets can adversely affect growth, and the nutritional impact on fish varies depending on the species of fly or mealworm. The advantages of soybeans are price, availability, and high protein content, while the disadvantages are the lack of essential amino acids, which affect the quality of the fish, and poor taste. An LCA study has been carried out on the proteins of black soldier fly, yellow mealworm and soybean. The most important impact categories from the PEFCR are climate change, particulate matter, acidification, land use, terrestrial eutrophication, and water use. The total single point value for the Black Soldier fly protein is 1.43E+01 µPt, the Yellow mealworm protein is 3.89E+02 µPt and the Soybean protein is 9.72E+01 µPt. The significant effect is due to the electricity consumption and the ingredients used in the feed. Sensitivity analysis was carried out for Black Soldier fly protein and Yellow Mealworm protein production where feed formulation was varied. In both sensitivity analyses, the lower environmental impact is from the new feed structure, which may be due to the use of food waste and wheat as feed ingredients in the new feed structure.

Applying life cycle assessment with minimal information to support early-stage material selection

Traditional life cycle assessment (LCA) is too data intensive and time consuming to be used during typical building design processes. Conducting an LCA during the building design process therefore requires simplifications and assumptions. Such “screening LCAs” are quicker and can be used with less data but introduce greater uncertainty. Unfortunately, uncertainty is not reflected in standard deterministic LCA calculations, which produce single-point values in LCA results. Thus, in this study, data quality scoring has been incorporated into a screening LCA to produce probabilistic predictions of environmental performance based on limited data. The approach has been applied during the design process of a bio-based wall panel designed for a circular economy. A combination of ecoinvent and material data sheets were used to analyse a wide range of novel bio-based insulation materials. The screening LCA analysed global warming potential and identified a short-list of promising materials that were then subjected to a detailed LCA for further consideration in the design. The method uses publicly available information and can be applied at material or building-element level. The method thus helps designers estimate environmental impacts without hindering the design process.

Validation of Soil Thermal Conductivity Models

This study describes the generation of a uniform data base of 2733 non-stationary thermal conductivity laboratory measurements of about 158 soil cores with varying texture, bulk density, soil organic matter, pH, and carbonate content. This data set has been used to validate ten well established pedo-transfer functions for predicting thermal conductivity by using easily available soil information such as soil texture, bulk density, and water content. Models were grouped into (i) physically based and (ii) empirical ones that need measured data for its calibration. The classical physical based transfer-function of deVries et al. has been finally chosen to set up a framework of standard values for the USDA soil classes. For planning purposes, these lambda estimates for selected pressure heads only need information on soil texture and bulk density and may be more valuable than single point values of thermal conductivity.

Circular shape MIMO antenna sensor for breast tumor detection

Abstract In this paper, a compact circular shape ultra-wide microstrip antenna is proposed for the detection of breast tumor. The proposed antenna is a two-port MIMO antenna of 1 × 2 elements. The dimensions of the proposed antenna are 34 mm × 18mm × 1.6 mm. It is designed over a lower-cost FR-4 epoxy substrate with a partial ground plane. The antenna is operated between the frequency range of 3.1–9.6 GHz. Isolation between the antenna element is less than −22 dB from 3.1 GHz to 7 GHz and −25 dB between 7 GHz and 10.6 GHz. The obtained ECC of the designed MIMO antenna is less than 0.01 and also DG is almost 10 dB in the entire UWB range. Further, the 3D breast phantom model is also simulated for analysis of the effect of SAR. Due to the variation in the electrical properties of cancerous cells and healthy cells it is possible to identify the cancerous tumor using SAR analysis. The obtained maximum Average SAR value without a tumor is 41.97 W/kg and with a cancerous tumor is 72 W/kg. Also, the variation in reflection coefficient helps to detect the tumor of the same composition but having different locations and having different sizes inside breast phantom. The principal component analysis is done to change the multi-variation in reflection coefficients data value to a single point value for better analysis.

Metrics of the Hadley circulation strength and associated circulation trends

Abstract. This study compares trends in the Hadley cell (HC) strength using different metrics applied to the ECMWF ERA5 and ERA-Interim reanalyses for the period 1979–2018. The HC strength is commonly evaluated by metrics derived from the mass-weighted zonal-mean stream function in isobaric coordinates. Other metrics include the upper tropospheric velocity potential, the vertical velocity in the mid-troposphere, and the water vapour transport in the lower troposphere. Seven known metrics of HC strength are complemented here by a metric of the spatially averaged HC strength, obtained by averaging the stream function in the latitude–pressure (φ–p) plane, and by the total energy of zonal-mean unbalanced circulation in the normal-mode function decomposition. It is shown that metrics, which rely on single-point values in the φ–p plane, produce unreliable 40-year trends in both the northern and southern HCs, especially in ERA-Interim; magnitudes and even the signs of the trends depend on the choice of the HC strength metric. The two new metrics alleviate the vertical and meridional inhomogeneities of the trends in HC strength. The unbalanced energy metric suggests a positive HC trend in both reanalyses, whereas the metric based on averaging the stream function finds a significant positive trend only in ERA5.

How circular is a value chain? Proposing a Material Efficiency Metric to evaluate business models

The concept of Circular Economy is a principle aiming to improve sustainable development by reducing resource use and impact on ecological systems. An increasing number of companies are applying this theory on design strategies and business models in order to close, slow and narrow material loops. To highlight the importance, guide practitioners, and evaluate the progress of circular economy, a high number of circularity metrics (C-metrics) have been developed. However, little attention has been given to creating a connection between quantification of circularity and environmental performance. Existing metrics also do not highlight the interplay between micro (product), meso (industrial symbiosis), and macro (regional) level circularity. Moreover, existing metrics do not capture all material loops and do not adopt a value chain perspective on material flows.To improve the connection between C-metrics and environmental performance, a framework connecting circular economy strategies and material flows was developed. Based on this framework, a material flow-based C-metric was designed aimed at converting mechanisms of closing, narrowing and slowing material loops into a single-point value. To evaluate its feasibility, the metric was tested on three circular business models that represent all three mechanisms in a value chain perspective. The results showed that the metric is feasible in more situations than existing metrics and that the circularity value is highly dependent on assumptions. In future studies, the metric should be tested and compared to Life Cycle Assessments on multiple system levels to ensure that it generates valid results. Furthermore, user input assumptions should be standardized to ensure metric reliability.

Fasted intestinal solubility limits and distributions applied to the biopharmaceutics and developability classification systems

After oral administration, a drug’s solubility in intestinal fluid is an important parameter influencing bioavailability and if the value is known it can be applied to estimate multiple biopharmaceutical parameters including the solubility limited absorbable dose. Current in vitro measurements may utilise fasted human intestinal fluid (HIF) or simulated intestinal fluid (SIF) to provide an intestinal solubility value. This single point value is limited since its position in relation to the fasted intestinal solubility envelope is unknown. In this study we have applied a nine point fasted equilibrium solubility determination in SIF, based on a multi-dimensional analysis of fasted human intestinal fluid composition, to seven drugs that were previously utilised to investigate the developability classification system (ibuprofen, mefenamic acid, furosemide, dipyridamole, griseofulvin, paracetamol and acyclovir). The resulting fasted equilibrium solubility envelope encompasses literature solubility values in both HIF and SIF indicating that it measures the same solubility space as current approaches with solubility behaviour consistent with previous SIF design of experiment studies. In addition, it identifies that three drugs (griseofulvin, paracetamol and acyclovir) have a very narrow solubility range, a feature that single point solubility approaches would miss. The measured mid-point solubility value is statistically equivalent to the value determined with the original fasted simulated intestinal fluid recipe, further indicating similarity and that existing literature results could be utilised as a direct comparison. Since the multi-dimensional approach covered greater than ninety percent of the variability in fasted intestinal fluid composition, the measured maximum and minimum equilibrium solubility values should represent the extremes of fasted intestinal solubility and provide a range. The seven drugs all display different solubility ranges and behaviours, a result also consistent with previous studies. The dose/solubility ratio for each measurement point can be plotted using the developability classification system to highlight individual drug behaviours. The lowest solubility represents a worst-case scenario which may be useful in risk-based quality by design biopharmaceutical calculations than the mid-point value. The method also permits a dose/solubility ratio frequency distribution determination for the solubility envelope which permits further risk-based refinement, especially where the drug crosses a classification boundary. This novel approach therefore provides greater in vitro detail with respect to possible biopharmaceutical performance in vivo and an improved ability to apply risk-based analysis to biopharmaceutical performance. Further studies will be required to expand the number of drugs measured and link the in vitro measurements to in vivo results.

Quantification of uncertainty in product stage embodied carbon calculations for buildings

Decarbonisation of the energy industry and enforcement of strict targets for operational energy consumption means that non-operational greenhouse gases (GHG) emissions, also known as embodied carbon (EC), will soon represent the majority of whole life carbon associated with buildings. EC assessments are often presented as deterministic, single-point values but contain a high degree of variability which is typically unacknowledged. Common sources of uncertainty are variability, data gaps, measurement error and epistemic uncertainty such as absence of detailed material specification (e.g. manufacturer, concrete mix, recycled content etc). Particularly during early design stages when such information is unconfirmed, average material data is used by necessity. While some material databases and LCA software can provide ranges of embodied carbon coefficients (ECC) between some materials and/or the uncertainty within individual manufacturers’ carbon data, the practice of reporting this is uncommon and has limited practicality for whole building assessments.This paper presents a simple procedure that selects the highest impact materials of the EC of an asset and implements a Monte-Carlo simulation to estimate the uncertainty behind the product stage EC assessment. Material coefficients of variation (CoV) are obtained from database values where available, and interpolated values are used in the absence of such data.A product stage EC assessment of a UK educational building, initially undertaken using single data points for each material, gave an EC prediction of 525 kgCO2e/m2 GIFA. Two scenarios were then assessed using our proposed procedure: 1) the full building scope and 2) substructure and superstructure only.It was demonstrated that, for scenario one, the EC can range from 50 to 140% of the original result when considering the extreme results from the Monte-Carlo simulation. Scenario one (considering the full building scope) resulted in an average EC value (mean ± CoV) of 526 kgCO2e/m2 GIFA ± 10.0%. The second scenario (sub- and super-structure only) resulted in an average EC value of 312 kgCO2e/m2 GIFA ± 11.9% with a full range of 45–155% of the original result.This paper shows that a straightforward uncertainty analysis procedure can support designers in understanding the possible range of asset product-stage EC and, therefore, inform construction product selections at an early stage where detailed information is not known. The variation also gives a degree of confidence/caution in the average EC prediction in lieu of a single-point result. The construction product CoV results can be used to set target ECCs on projects to help ensure reliable low-carbon products are specified. If these target ECCs were met, a minimum of 29% and 33% (excl. EPD uncertainty) in product stage EC reductions could be achieved. Future work should extend this method to include additional life cycle assessment (LCA) stages and other uncertainty factors. And, the method could be applied to comparative life cycle assessments and optioneering exercises, as well as including more specific construction product variability data.

Association of fluid balance trajectories with clinical outcomes in patients with septic shock: a prospective multicenter cohort study

BackgroundSeptic shock has a high incidence and mortality rate in Intensive Care Units (ICUs). Earlier intravenous fluid resuscitation can significantly improve outcomes in septic patients but easily leads to fluid overload (FO), which is associated with poor clinical outcomes. A single point value of fluid cannot provide enough fluid information. The aim of this study was to investigate the impact of fluid balance (FB) latent trajectories on clinical outcomes in septic patients.MethodsPatients were diagnosed with septic shock during the first 48 h, and sequential fluid data for the first 3 days of ICU admission were included. A group-based trajectory model (GBTM) which is designed to identify groups of individuals following similar developmental trajectories was used to identify latent subgroups of individuals following a similar progression of FB. The primary outcomes were hospital mortality, organ dysfunction, major adverse kidney events (MAKE) and severe respiratory adverse events (SRAE). We used multivariable Cox or logistic regression analysis to assess the association between FB trajectories and clinical outcomes.ResultsNine hundred eighty-six patients met the inclusion criteria and were assigned to GBTM analysis, and three latent FB trajectories were detected. 64 (6.5%), 841 (85.3%), and 81 (8.2%) patients were identified to have decreased, low, and high FB, respectively. Compared with low FB, high FB was associated with increased hospital mortality [hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.22–2.17], organ dysfunction [odds ratio (OR) 2.18, 95% CI 1.22–3.42], MAKE (OR 1.80, 95% CI 1.04–2.63) and SRAE (OR 2.33, 95% CI 1.46–3.71), and decreasing FB was significantly associated with decreased MAKE (OR 0.46, 95% CI 0.29–0.79) after adjustment for potential covariates.ConclusionLatent subgroups of septic patients followed a similar FB progression. These latent fluid trajectories were associated with clinical outcomes. The decreasing FB trajectory was associated with a decreased risk of hospital mortality and MAKE.

Efficient video-based breathing pattern and respiration rate monitoring for remote health monitoring.

A contact-free inexpensive measurement system with an algorithm based on the integral form of video frames is proposed to estimate the respiration rate from an extracted respiration pattern. The proposed algorithm is applied and tested on 28 videos of sleeping-simulated positions, and the results are compared with the manual visual inspection values. With linear regression, the determination coefficient (R2) is 0.961, which demonstrates high agreement with reference measurements. In addition, the Bland-Altman plot shows that almost all data points are within the 95% limits of agreement. Moreover, the time complexity of the proposed algorithm, which involves taking just a single point value of the integral image, is lower than that of traditional methods that circulate over a large number of points. In other words, the proposed algorithm achieves O(1) fixed-time complexity compared to O(N2) for traditional methods. The average speed of processing is enhanced by about 17.4%.

Time history of upper-limb muscle activity during isolated piano keystrokes

Playing-related musculoskeletal disorders (PRMDs) in pianists can lead to the cessation of performance-related activities. A better understanding of the impact of performance parameters on muscle activities could help improve prevention of pianists’ PRMDs. The purpose of this study was to assess the effect of touch and articulation (two performance parameters) on muscle activity and to compare analysis based on scalar and time-history values. Activity of nine upper-limb muscles were recorded in 12 professional classical pianists during the performance of slow-paced isolated keystrokes using pressed and struck touch and staccato and tenuto articulation. A two-way (touch and articulation) ANOVA with repeated measures was performed on time history and single-point values of muscle activations. Pressed touch prime mover muscle was the triceps brachii while struck touch entailed progressive deactivation of anti-gravity muscles before the keystroke. Compared to tenuto articulation, staccato articulation induced a muscular burst on shoulder muscles. Our results suggest that warm-up routines aiming to prevent PRMDs should integrate different types of touch and articulation. Staccato articulation appears however to be an important risk factor of PRMDs located at the shoulder structure. Temporal analysis was a more reliable tool to interpret pianists’ muscle activity during keystrokes.

Prestack time migration velocity analysis using recurrent neural networks

We present a new efficient method to perform prestack time migration velocity analysis (MVA) of seismic data based on recurrent neural nets (RNNs). We assume that there exits a mapping from each local data subvolume, which we term an analysis volume, to each single velocity point value in space. Under this hypothesis, as RNNs are capable of learning structural information both in time and in space, we propose designing a net that can learn this mapping, and exploit it to produce the entire root-mean-square (rms) velocity field. The performance of the method is evaluated via real data experimental results in comparison to existing technique for MVA. We present different aspects of the system's behavior with various training and testing scenarios and discuss potential advantages and disadvantages. We also recast the same RNN to learn and automate migration velocity picking from constant velocity migration (CVM) panels and compare real data results for this alternative. Our approach is extremely efficient in terms of computational complexity and running time, and therefore can be potentially applied to large volumes of three-dimensional (3D) seismic data, and significantly reduce work load. An extension to four-dimensional (4D) data is also possible.

The effect of value on judgment of learning in tradeoff learning condition: the mediating role of study time

Metamemory refers to the metacognitive awareness of one’s own memory status. Previous research has shown that item value plays a dominant role in self-regulated study (e.g., strategic choices regarding when, what, and how to study). In spite of extensive research on the effects of item value on in learners’ study behaviour, less is known about the effects of item value on judgments of learning (JOLs; i.e., metacognitive predictions about the likelihood that a given item can be remembered at a future occasion), especially when item value and item difficulty conflict. Three experiments were conducted in the present study to examine whether the effect of item-value can override the effect of item difficulty on JOLs. In each trial, three word pairs, associated with varying difficulty levels, were presented simultaneously, and the three pairs were all associated with a single point value in the constant-value condition, but with different point values in the tradeoff condition. Point values were presented alongside the word pairs. After the learning phase, participants made item-by-item JOLs. Experiment 1 demonstrated that JOLs systematically decreased with the increase of item difficulty in the constant value condition. Critically, item-value eliminated (but failed to reverse) people’s preference for easy items in the 1–3-6 tradeoff condition. Experiment 2 expanded the value gradient and found a full reversal, such that JOLs were primarily influenced by item value. Experiment 3 measured study time allocation to explore the mechanisms underlying learners’ metacognitive monitoring process. A mediation analysis showed that the direct effect of value on JOLs was apparent only when value was highly salient; with lower value salience, the effect of value on JOLs was entirely mediated by changes in study time. The findings of the present study further complement the agenda-based regulation (ABR) model on value-directed metacognitive monitoring. An analytic method to examine the function of a particular cue on JOLs for the cue-utilization hypothesis is also provided.

Reliability Evaluation on a Joint Machine Learning and Optimization Framework

Coordinately embedding machine learning and optimization in the Monte Carlo simulation is proposed as a new framework for reliability evaluation, which is becoming more challenging due to the joining of variability and intermittency of the renewable generation and components’ ageing. As a machine learning method, the dynamic Bayesian belief network predicts the renewable outputs by generating their probability distributions through historical data to overcome the defect of rarely grasping the low-probability events in the traditional methods, which either predict a single-point value or presume a parametric probability distribution. As the internal operation module of the framework, the rolling-horizon unit commitment maintains the complexity of the operation model and meantime punctually updates the predicted renewable generation and the components’ ageing. The dynamic Bayesian belief network and the rolling-horizon unit commitment traverse time step after time step throughout the horizon, and thus form one bid of the Monte Carlo simulation. In each time step, the dynamic Bayesian belief network extracts the probability distributions of the renewable generation, from which the boundary of the commitment is sampled, and the impact of scheduling the generators on their ageing is then accumulated from the commitment. The proposed framework has its effectiveness proved on a microgrid.

Geospatial assessment of land surface temperature in Nagpur, India: an impact of urbanization

Temperature is one of the factors which affects the meteorological phenomenon prevailing in an urban area and ultimately leads to climate change and global warming. Measurement of surface temperature with conventional temperature sensors is tedious, has single point value and expensive. Satellite data of 2005, 2008, 2010 and 2016 are used to assess the land use land cover (LULC) and to measure the land surface temperature (LST) during the hottest month of May in Nagpur city, India. Based on the image analysis, it is observed that there is an increase in the average temperature from 40.0 to 44.6 oC. This may be due to an increase in built-up area from 55.5 to 69.8% during 2005 to 2016, respectively. It has also been revealed that the outskirts of the city are hotter than the central portion of the city as there is more barren land on the outskirts. Results were compared to ambient temperature sensor that showed good agreement between temperature retrieved from satellite and temperature sensor. The study suggests that urban areas should be developed intermixed with vegetation and plantation and provisions of green belt along the city roads, highways and ring roads. Based on LST and LULC analysis, a green belt of 30 m along both sides of the road would lower the road temperature by 9.3 oC from existing temperature of 44.7 oC. This would help in lowering down the average temperature of the city.